What’s new or different#

NumPy 1.17.0 introduced Generator as an improved replacement for the legacy RandomState. Here is a quick comparison of the two implementations.

Feature

Older Equivalent

Notes

Generator

RandomState

Generator requires a stream source, called a BitGenerator A number of these are provided. RandomState uses the Mersenne Twister MT19937 by default, but can also be instantiated with any BitGenerator.

random

random_sample, rand

Access the values in a BitGenerator, convert them to float64 in the interval [0.0., `` 1.0)``. In addition to the size kwarg, now supports dtype='d' or dtype='f', and an out kwarg to fill a user- supplied array.

Many other distributions are also supported.

integers

randint, random_integers

Use the endpoint kwarg to adjust the inclusion or exclusion of the high interval endpoint

  • The normal, exponential and gamma generators use 256-step Ziggurat methods which are 2-10 times faster than NumPy’s default implementation in standard_normal, standard_exponential or standard_gamma. Because of the change in algorithms, it is not possible to reproduce the exact random values using Generator for these distributions or any distribution method that relies on them.

In [1]: import numpy.random

In [2]: rng = np.random.default_rng()

In [3]: %timeit -n 1 rng.standard_normal(100000)
   ...: %timeit -n 1 numpy.random.standard_normal(100000)
   ...: 
1.2 ms +- 23.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
2.24 ms +- 16.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
In [4]: %timeit -n 1 rng.standard_exponential(100000)
   ...: %timeit -n 1 numpy.random.standard_exponential(100000)
   ...: 
619 us +- 11.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
1.59 ms +- 7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
In [5]: %timeit -n 1 rng.standard_gamma(3.0, 100000)
   ...: %timeit -n 1 numpy.random.standard_gamma(3.0, 100000)
   ...: 
2.34 ms +- 8.08 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
4.36 ms +- 15.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
  • integers is now the canonical way to generate integer random numbers from a discrete uniform distribution. This replaces both randint and the deprecated random_integers.

  • The rand and randn methods are only available through the legacy RandomState.

  • Generator.random is now the canonical way to generate floating-point random numbers, which replaces RandomState.random_sample, sample, and ranf, all of which were aliases. This is consistent with Python’s random.random.

  • All bit generators can produce doubles, uint64s and uint32s via CTypes (ctypes) and CFFI (cffi). This allows these bit generators to be used in numba.

  • The bit generators can be used in downstream projects via Cython.

  • All bit generators use SeedSequence to convert seed integers to initialized states.

  • Optional dtype argument that accepts np.float32 or np.float64 to produce either single or double precision uniform random variables for select distributions. integers accepts a dtype argument with any signed or unsigned integer dtype.

In [6]: rng = np.random.default_rng()

In [7]: rng.random(3, dtype=np.float64)
Out[7]: array([0.22090009, 0.0794563 , 0.81870326])

In [8]: rng.random(3, dtype=np.float32)
Out[8]: array([0.64836   , 0.12747508, 0.28917038], dtype=float32)

In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)
Out[9]: array([ 79, 189,  92], dtype=uint8)
  • Optional out argument that allows existing arrays to be filled for select distributions

    This allows multithreading to fill large arrays in chunks using suitable BitGenerators in parallel.

In [10]: rng = np.random.default_rng()

In [11]: existing = np.zeros(4)

In [12]: rng.random(out=existing[:2])
Out[12]: array([0.24329996, 0.4593383 ])

In [13]: print(existing)
[0.24329996 0.4593383  0.         0.        ]
  • Optional axis argument for methods like choice, permutation and shuffle that controls which axis an operation is performed over for multi-dimensional arrays.

In [14]: rng = np.random.default_rng()

In [15]: a = np.arange(12).reshape((3, 4))

In [16]: a
Out[16]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

In [17]: rng.choice(a, axis=1, size=5)
Out[17]: 
array([[ 2,  2,  3,  3,  1],
       [ 6,  6,  7,  7,  5],
       [10, 10, 11, 11,  9]])

In [18]: rng.shuffle(a, axis=1)        # Shuffle in-place

In [19]: a
Out[19]: 
array([[ 1,  0,  2,  3],
       [ 5,  4,  6,  7],
       [ 9,  8, 10, 11]])
  • Added a method to sample from the complex normal distribution (complex_normal)